Directed Alert Graph for Database Root Cause Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Database administrators face inefficiencies in determining the root cause of abnormal operation indicators, requiring manual analysis of multiple indicators, which leads to prolonged time consumption and instability in database services.
Innovation Solution
A data processing method that generates a directed alert graph to visually display causal relationships between abnormality indicators and their propagation relationships, allowing for intuitive identification of root causes and quick resolution of issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of multiple operation indicators is performed to determine root cause, then measurement precision of root cause identification is improved, but time consumption increases significantly
Solution Approach 1:
The patent introduces an abnormality propagation relationship as an intermediary mechanism that connects abnormal operation indicators to root causes. Instead of requiring DBAs to manually analyze multiple indicators, the system uses pre-established propagation relationships as a mediator to automatically trace from symptoms to root causes, significantly reducing analysis time while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary action by pre-establishing abnormality propagation relationships between operation indicators and root causes before actual failures occur. This pre-computed knowledge base allows the system to quickly determine root causes during actual incidents without requiring real-time manual analysis of multiple indicators.
2Reliability
If multiple operation indicators are monitored and analyzed, then reliability of database system monitoring is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex monitoring system into two distinct parts: (1) a monitoring component that collects operation indicator data, and (2) an abnormality propagation relationship database that stores pre-computed causal relationships. This segmentation allows the system to maintain comprehensive monitoring reliability while reducing the complexity of real-time analysis by separating data collection from root cause determination.
Solution Approach 2:
The abnormality propagation relationship serves as an intermediary layer between the monitored operation indicators and the root causes. This intermediary structure simplifies the analysis process by providing pre-established causal links, reducing the complexity of analyzing multiple indicators while maintaining monitoring reliability.
3Measurement precision
If manual root cause analysis is performed, then measurement precision of abnormality detection is improved, but productivity of database maintenance decreases
Solution Approach 1:
The patent enables the system to perform self-service by automatically determining root causes using pre-established abnormality propagation relationships. Instead of requiring DBAs to manually analyze each abnormality, the system uses its own pre-computed knowledge base to automatically trace and identify root causes, significantly improving maintenance productivity while maintaining detection accuracy.
Solution Approach 2:
The system performs preliminary action by pre-computing abnormality propagation relationships during system operation. This advance preparation allows the system to automatically and quickly determine root causes during actual incidents without requiring manual analysis, thereby improving both accuracy and productivity.
Data Source
AI summary
A data processing method, an apparatus, a database system, an electronic device, and a storage medium are disclosed. The data processing method includes obtaining problem analysis result data generated according to abnormality indicators of a database system, wherein the problem analysis results include associated abnormality indicators that are related in abnormality indicators and abnormality propagation relationships between the associated abnormality indicators; generating a directed alert graph used for displaying causal relationships between causes of abnormality corresponding to the associated abnormality indicators and abnormal phenomena based on the associated abnormality indicators and the abnormality propagation relationships between the associated abnormality indicators; and displaying the directed alert graph for giving an alert. Using the embodiments of the present disclosure can give an alert more intuitively and efficiently.